A novel dimensionality-reduction approach for face recognition

In this paper, we propose a novel dimensionality-reduction method—Fisher discriminant with Schur decomposition (FDS). Similar to Foley–Sammon discriminant analysis (FSD), FDS is an improvement of Fisher discriminant analysis (FDA) in that it eliminates linear dependences among discriminant vectors. In comparison with FSD, FDS is very simple in theory and realization. Experimental results conducted on two benchmark face-image databases, i.e. ORL and AR, demonstrate that FDS is highly effective and efficient in reducing dimensionalities of facial image spaces. Especially when the size of a database is large, FDS can even outperform the state-of-the-art facial feature extraction methods such as the null space method.